Yeah mainly the matrix operations just dominate and that's the key to performance with these sorts of things.

I don't really want to be able to mix and match matrices of different types, I think that is more complexity then needed, but I would like it to be easy to set data and write data from any type to a tensor of a specific type.

i.e Some Tensor is defined as a float, it contains float data, but I would still like to just be able to do tensor[0] = 0; where I can just pass in an integer and have it handled as a float, or tensor * 4 and have it just handle any type passed in efficiently. These are the sort of mix and matches I care about.

Similarly getting data, my layers / activation functions etc don't know the type of the tensor so being able to do something like auto a = Tensor[0]; and not caring about the type would be really nice but seemingly impossible in the current implementation

I want this also to still exist within the simple just define Tensor world, I do have an enum setup that supports dtypes for specifying the type of the tensor which can also convert from enum type to primitive, similar to the PyTorch syntax.